How I would learn Machine Learning (if I could start over)
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- čas přidán 6. 06. 2024
- In this video, I give you my step by step process on how I would learn Machine Learning if I could start over again, and provide you with all recommended resources.
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#MachineLearning #DeepLearning
0:00 Introduction
1:01 MATH
1:58 PYTHON PYTHON
2:37 ML TECH STACK ML TECH STACK
3:35 ML COURSES ML COURSES
4:44 HANDS-ON & DATA PREPARATION
5:17 PRACTICE & PRACTICE & BUILD PORTFOLIO
6:16 SPECIALIZE & CREATE BLOG
1. Math 1:00
2. Python 2:00
3. Machine Learning TECH STACK 2:37
4. Machine Learning Courses 3:35
5. Hands - On & Data Preparation 4:45
6. Practice & Build Portfolio 5:19
7. Specialize & Create Blog 6:15
Awesome! Thank you for sharing.
Thank you. May your thoughtfulness be rewarded a thousand times.
… but the videos has chapters.. 🤨
W pfp
Thank you
learning machine learning is quite fun
This is just what I was looking for! I was overwhelmed with the amount of resources out there, so it is incredibly useful to have a solid roadmap going forward. Thank you!
all you had to do was to poop and drink some coffee...
Very effective steps! I have been following this roadmap for the past couple of months, and I am happy with the progress I have made
(Note to Self - How I would learn Machine Learning)
01:00 1. Math: Khan Academy
Recommended Courses:
- Multi-Variable Calculus
- Differential Equations
- Linear Algebra
- Statistics and Probability
02:00 2. Python
Recommended Courses
- FreeCodeCamp: Python in 4-Hours Full Course
- FreeCodeCamp: Intermediate Python in 6-Hours
02:37 3. Machine Learning TECH STACK
Most important Python libraries for Machine Learning, Data Science, and Data Visualization
Optional: Can be picked up later when doing the ML course.
Use for every project, which is why he recommends doing them now to build a base.
Follow a free crash course for now, pick up more advanced concepts later if needed.
- NumPy: Base for everything: Python Engineer - NumPy Crash Course Complete Tutorial
- Pandas: Data handling: Keith Gali - Complete Python Pandas Data Science Tutorial
- MatPlotLib: Visualization: FreeCodeCamp - MatPlotLib Crash Course
--------------------------- The following MachineLearning courses aren't yet needed
- Tensor Flow
- Scikit Learn
- PyCharge ???
03:35 4. Machine Learning Courses
- Machine Learning Specialization by Andrew Ng (Coursera)
- Implement algorithm from scratch using his 'ML from SCRATCH' playlist
- ML from Scratch Playlist by Python Engineer (Assembly AI)
04:45 5. Hands - On & Data Preparation
Kaggle Courses
- Intro to Machine Learning
- Intermediate Machine Learning
05:19 6. Practice & Build Portfolio
Kaggle: Competitions
- They provide lots of datasets, platform to evaluate, and a community.
06:15 7. Specialize & Create Blog
- NLP
- PyTorch / Tensor Flow
- MLOps
06:52 Start a VLOG
- Tutorial
- Share what you've learned
- Share the projects you've built
- Problems faced and how you have solved them
- Write about a topic
07:24 Books
- Machine Learning with PyTorch and SckiKit-Learn by Raschka
- Hands-On Machine Learning with SciKit-Learn, Keras & TensorFlow by Geron
Gud
Thank you!!
Machine Learners are busy people. Your comment proves you understand that!
Ok
Best comment!
Thank you
This outline is phenomenal - thank you!
One of the most luxurious advice I've ever heard ( or watched)
Thank you Patrick
One of the most luxurious pieces of advice I've ever heard ( or watched)
Thank you, Patrick.
Nice, I was struggling to decide what to learn first? This field is so overwhelming for beginners. Thanks for explaining out everything so clearly.
Very welcome!
How did you proceed?
Hi Puneet, how did you learning ML go? I am starting to explore my options in this area.
Hey did you learn any math
Great roadmap Patrick! It would be great if add few examples projects to practise. Most of the ML learners find it challenging to find projects.
Fantastic. Short, to the point and clear!
This outline is phenomenal - thank you!. This outline is phenomenal - thank you!.
I really value this plan...you don't understand. There's so many people who quit at the jump because people in the industry give very broad steps. This is a very clear plan with flexibility to go even deeper into each resource and step. Also, for starters, you even said 3 months. Some may say that is unrealistic but as a Math major with no CS experience but a heavy interest in AI theoretically, the drive is already there. Learning can't be rushed but it can definitely be integrated quickly with the right resources. I plan on putting at least 10 hours each week into this journey. Thanks again man!
How's it going so far?
How’s your learning journey been ? You must be at the end of it. Give us an update. I am planning on joining the same journey
Give an update bro, I'm also Math major with a lot of interest in AI and on this journey rn.
@@ameynarwadkar7924 well, since OP is MIA, I'll give you my update. For context, I have a bachelor's in electromechanical engineering, so I skipped the math courses for now. I also have a ton of experience using MatLab, so I already have a solid fundamentals on coding logic, objects, and loops.
Since I left my comment, I've gotten through the beginner python courses, half of the intermediate Python course, and I'm starting on the ML Tech Stack this weekend.
The beginner python course was very helpful. He goes through some of the Python fundamentals by coming up with real-world problems, and then using the concepts he shows you to solve those problems. A word of advice: after he explains what he's about to do at the beginning of each tutorial, pause the video, and see if you can do it yourself. Be persistent. Then play the video, and compare what you built to what he shows you. It will take you much longer to get through the video this way, but I think it's a much more effective way to learn for most people.
After the beginner course, I refined some of the basics by building my own simple programs of things I came up with. Example: I built a program that calculates a list of prime numbers in a user defined range, I wrote a script that approximates pi using a random number generator, I wrote an algorithm that calculates the largest number in a list of randomly generated integers.... Stuff like that. Simple logic puzzles that will help build your confidence and refine some of the basics in a practical context.
I got about halfway through the "intermediate" video and realized it was kind of a waste of time for me. He doesn't actually discuss any intermediate concepts. He just lists off a whole bunch of miscellaneous functions that you may or may not use. He doesn't discuss where the functions would be used, or demonstrate how to solve a problem using the functions.... He just explains the function's syntax, and moves on. And frankly, I'm not going to remember 90% of it anyway, so I decided to skip the rest. I figure if there's a new function I need to use in the future, I'll just Google the syntax and proper use when I need it. But that's just me, and how I learn. If you're one of those people who have a photographic memory, or you plan on making syntax flashcards or something, then maybe this video will be useful to you. But personally I don't learn that way.
The "ML Tech Stack" I'm just starting now, so I can't really speak to that yet. I plan on breezing through that pretty quickly. And I can give you another update once I start the actual ML stuff.
Tell us where u at?
Thank you Patrik!!! Amazing intro for ML topic🙏🙏
Great video! I was completely lost on how to start learning about AI. I am a finance major, and I realized that if I don't learn it now, I will probably get left behind.
Thanks for the advice. I’m going to apply your approach in my learning. It sounded feasible and well-though 🙏
Awesome. Go for it. Can’t wait to hear updates.
Thanks for the video. I have learned lots of ML-related stuff in the past several months, but I feel like the way I have learned is NOT the the best way. The way you suggested makes more sense.
Youre awesome, no bullshit, litteraly just helping people, thank you.
Thank you so much Patrick for your insights and guidance. The links you have provided are really helpful. Thanks again!
Thanks for this. I’ve got a physics ms, so I have a lot of the math, and some basic python(I enjoy Python but really only had time to learn it while using it for physics stuff), and I’ve been looking at where to go next to better understand machine learning.
True and honest roadmap, thanks a lot!!
Very helpful. Thanks for sharing your experiences
Amazing how machine learning algorithm in CZcams works, I was just thinking about ML earlier today and in the evening I got this video recommendation. ☀🔨
that is mind learning, not machine learning
Awesome content ! Thank you so much for sharing it
Thank u! This is a great roadmap.
The blog tip is great! Gonna use that for sure inshAllah :)))
This is exact i was looking for. Thank you so much
starting this roadmap from today. wish me luck!
hope everyone else also achieves their goal.
hello Dude, whats ur progress?
Wonderful video! Very useful information was presented :)
thank you :)
Thank you for making this video. It's very helpful.
This is a very good guideline. Thank you.
Very Good Explanation. Great Video. Thank You for you ❤
Thanks a lot for this detailed breakdown.
Nice informative video and helped me build a roadmap. Thanks!!!
Thank you so much. This is extremely valuable.
Glad you think so!
Thank you for the video, I was lost on how to start
Thanks man, it’s very helpful
Amazing sir, thanks much. Please do more.
Very helpful, indeed.Thank you!
Now i know where to learn and in what order TY
Also starting with this roadmap today, for now refreshing math. Hope I can get through this 100%!
How is it going bro?
@abdel8819 slowly! But made some progress, started with Andrew Ng courses and trained my first model already;)
Hi Sir, Thanks so much for this roadmap. I want to learn AI & Machine Learning but I had no idea on how to do so. But your video explains everything I need to do. Thanks so much.
Can we connect? I want to learn also, and a learning partner won’t hurt
@@jamesojih8050 I'm currently busy learning other technologies. So, I wont be able to start on AI at this moment.
Great Guidance, thanks a lot!!
Sir, thank you for this video. Sir you are very inspiring.
Great advice!!
Thanks for the great learning plan. I would just add that for Multivariable Calculus, Single variable calculus is needed. And as an option instead of "Statistics Probability" i would use an ordered learning path: "Combinatorics -> Probability -> Statistics"
Hi Andrey, are you already an ML Engineer?
@@adekanbioluwaseun219 Hi. Not yet. I would say i've just start to learn the Math and Python. I am not sure i will became a ML engineer, but along the journey i will definitively pick up a lot of skills.
@@andreypopov6166 I think the same but I'm just starting, for now, should i just do the courses patrick mentioned in order?
Hi. How long did it took you guys to complete the math studies
Thank you for your work!
Awesome incredible advice! TQ so much
This was helpful. Subscribed
Really good video for beginners in AI like me at least :)
Great video, thank you so much.
Very clear and an informative video
Very helpful, thank you
Your suggestion to create a blog is simply genius.
very helpful, thank you!
Yes, this was helpful. Thank you very much.
Glad it was helpful!
Heads up regarding the math course recommendations - you can't just do things like Multivariable Calculus out of the blue without proper background. That's the equivalent of Calculus 3 at my school, so I recommend completing Calculus 1 and 2 before knocking out the Multivariable course or any of the others for that matter - best of luck knocking out the course requirements!
Omg perfect for my self made pre-master!!
I liked this video and saved it. Made me also notice something about IT people: they don't breathe! I was listening to an IT specialist on TV yesterday, he didn't even listen to the Qs of the interviewer my head hurts its even unsettling
This is a great video. Thank you 😊
Yep😊
its very helpful and thanks for the information
Thx, You are amazing!
Trying out this roadmap March 1st 2023. Will update everyone 6months from then. I’m already a software engineer so I’ll be skipping the coding steps and the math will be refreshers but far from a data scientist or data analyst for that matter. Hope everything works out. See you guys in the future!
Good luck 🤞 Commenting so I can see the updates
Yessir good luck!!
yes
Leetssssss go!!!
all the best bro, you will make it big, ik it!!
Thanks this is exactly what I have been looking for! BTW what would you recommend (or would these courses already be enough?) if I wanted to basically teach a robot arm to do stuff? I want to build a robot arm with micro-servos and something like a raspberry pi, and then I want to be able to basically practice machine learning by teaching the arm to complete tasks. Do I have to maybe create some sort of cgi version, teach it, then upload the code to the pi? Or is this something I will know how to do after taking all the courses you recommend? I know I'm very beginner here, but I just would very much like to be pointed in the right direction for this. TIA
Nice job - can you do a deep learning path ? You
thanks for teaching us 👍
Thanks 👍🏻
finally i now undrestand machine learning well
thank you so much!!!!!!!!!!!
My leaning map is below,hope it can help anybody
i begin my python learning in 12,2022. I quickly read a book in 72h. After that I began to learn on Kaggle. I make my coding skills better(some basic pandas numpy and matplotlib) and learn some basic ml.
After that I find that my data analysis skill is not good enough to clean the data. I read the book called python for data analysis. I finished it in 2,2023.
From 3,2023 to now I am reading the ml part of hands on ml. I hope to finish it in next week. This book give us some real world views. After that it’s time to learn the math inside the ml.
Nice one!
This video is amazing
This man just single handedly planned my life, what a legend!
Agreed my friend
many Thanks!
Intro to Statistical Learning by Gareth James and others is a great book for learning the statistical part for basics.
Thank you bruh too useful
Thank you sir!!!
Thank you for this video...
can you make a video elaborating how to build a more effective portfolio in ml and which platform to use?
Helpful !!!
thanks bro. great
I think the most underrated part is the math. I myself study Artificial Intelligence in university, which is a bit different and more advanced than simply machine learning. We take 12 courses upfront before starting 'the real deal' machine learning. We learn linear algebra, calculus, bayesian statistics, logic and I absolutely love the way our major is structured in this way, because now that we're doing machine learning, everything makes sense and with this knowledge you really learn on what data you can apply which model. You don't learn that online. They simply say: "for these problems, you simply use these models", which is okay for data scientists, but not for people who study AI themselves within the research field.
Can you share the link for the course curicculum or syllabus?
what university do you go to
Great video :)
Question: For the sake of having a mentor / tutor, do you recommend taking a pricey course on, say Interview Kickstart? They have an $11,000 full AI course that has tutors who work in the industry there to help you at least 3 days a week. For the sake of organization and knowing for sure you're learning the right stuff, I can see that being a good thing, but can this stuff still be learned well enough for much less money to get a job in the industry? Would love you know your thoughts and anyone else's reading this - thanks!
In this day and age there are way to many options and opinions out there that don't make sense - it was nice to get a clear and concise short take on what to do. Even though I am a senior in college and have a decent amount of personal experience with programming and full stack web dev and some industry experience with those, I'll be following all these steps to make sure I get the best foundation possible.
I'm a programmer specializing in life-critical software. I'd like to apply that experience specifically to the AGI alignment problem, not ML in general. Is your advice pretty much the same in my case?
thank you so much, and also I want to know how much time will take to become ML engineer and ready for a job
Any science, engineering, technology field will require a foundation of strong math skills, so it's VERY important to brush up on these skills or learn them in a university setting before moving on to the next steps of programming in python. Then you'll learn about python data structures and apply what you've learned to implementing machine learning algorithms. And so on!
Subscribed!
Thank you so much!
Very good recommendation, I find mL very intersting . Do I have to be good in statistics to make a career in Machine Learning Engineering ?
I will love you forever because of this video.
You do not need to know the math to learn machine learning,, however, you will never be a pioneer in machine learning if you do not know the math.
me: yes! no crazy math required!
also me: completes your comment 😮🫣
@@yoursubconscious And "a little math" is a joke, you need Math, Physics or CS degree. If you don't have a science degree I would really suggest picking another field. Picking ML without learning at least economy level math is like being a short basketball player. I have (well, almost) a CS masters + as much experience as the guy in the video and plan to study an additional Math degree. The only option to skip it is to self study a lot of Math, but I consider it the same as having a degree. Truth is, most people from humanities think that advanced math is something else than it really is, so they might think they studied math, yet they have 0 intuition. TLDR go study Math or go be a software engineer, it might be even more interesting.
@@heyman620 - every helpful and intuitive. 🙏 thank you, really!
Currently I am a PhD maths student.
But I am not into this coding thing
I am going to start learning this ML and AI as I need these all for some research purpose .
Are all these things too difficult
Like coding ai and Ml?
Ok, so you tottaly don't need to derive the error with respect to the trainable parameters to see how you should update the parameters, nor should you learn linear algebra to understand crosscorrelation or convolution or dot products or matrix multiplication, nor shall you learn probability. just stop capping 🧢🧢🧢.
Hi Patrick. Many thanks for your insights. I'm currently planning a self paced curriculum to achieve an undergraduate level of either computer science or data science with the specialization as machine learning engineer at the end. I am committed to the Open Source Society University (OSSU) which offers both curriculums following the ACM/IEEE curriculum recommendations for undergraduate degree programs in CS and DS. My problem is, that they don't have a specific curriculum for a specialization on AI and machine learning. This is why I'm thankful for your tips in regard to this field and I appreciate your videos to help me finding the best supplemental materials for my planning. Looking forward to see more of your content in the coming months an with my own pace eventually years. Take care and till later.
Based on your experience in the field, would you recommend starting with TensorFlow/Keras or Pytorch? I know its not a big decision to make now, but its still good to start with something that is prevalent, I guess, and offers more learning materials.
Thank you sir.
can you do the video on MLops, thank you for this amazing video, grateful
Thanks Sir 💖
thanks you are a king
Thank you.
subscribed! yey thanksss1 :D
Hi Patrick, I'm concerned about ( Machine Learning Courses 3:35 ) the specialization is not free unless you take courses one by one and audit them. Is that what you meant!!! Auditing the course... since auditing means I can't participate in the homework and projects??? What is your recommendation for either auditing the course and practicing from your playlist (Machine Learning from Scratch - Python Tutorials) or enrolling and paying the tuition in Coursera to fully utilize the course tools, such as projects? Thanks
you can apply financial aid, and you’ll get the course for free in 15 days
@@arunachalamm3399 use chatgpt to apply and every year I get 10 courses for free(WHICH IS MAX)
Excellent presentation, guidance and detailed analysis about how to gain mastery of the tools and technology of ML. Thank you sir.